Mean function in R -mean() calculates the arithmetic mean. mean() function calculates arithmetic mean of vector with NA values and arithmetic mean of column in data frame. mean of a group can also calculated using mean() function in R by providing it inside the aggregate function. with mean() function we can also perform row wise mean using dplyr package and also column wise mean lets see an example of each.
- mean of the list of vector elements with NA values
- mean of a particular column of the dataframe in R
- Mean of multiple columns of a dataframe in R
- column wise mean of the dataframe using mean() function
- mean of the group in R dataframe using aggregate() and dplyr package
- Row wise mean of the dataframe in R using mean() function
Syntax for mean() function in R:
- x – numeric vector
- rm- whether NA should be removed, if not, NA will be returned
Example of R Mean() function:
# R mean function x <-c(1.234,2.342,-4.562,5.671,12.345,-14.567) mean(x)
output:
Example of R Mean() function with NA:
Mean function doesn’t give desired output, If NAs are present in the vector. so it has to be handled by using na.rm=TRUE in mean() function
# mean function for input vector which has NA. x <-c(1.234,2.342,-4.562,5.671,12.345,-14.567,NA) mean(x,na.rm=TRUE)
output:
Example of mean() function in R dataframe:
Lets create the data frame to demonstrate mean function – mean() in r
### create the dataframe
my_basket = data.frame(ITEM_GROUP = c("Fruit","Fruit","Fruit","Fruit","Fruit","Vegetable","Vegetable","Vegetable","Vegetable","Dairy","Dairy","Dairy","Dairy","Dairy"),
ITEM_NAME = c("Apple","Banana","Orange","Mango","Papaya","Carrot","Potato","Brinjal","Raddish","Milk","Curd","Cheese","Milk","Paneer"),
Price = c(100,80,80,90,65,70,60,70,25,60,40,35,50,120),
Tax = c(2,4,5,6,2,3,5,1,3,4,5,6,4,3))
my_basket
so the resultant dataframe will be
mean of a column in R data frame using mean() function :
mean() function takes the column name as argument and calculates the mean of that particular column
# mean() function in R : mean of a column in data frame mean(my_basket$Price)
so the resultant mean of “Price” column will be
output:
column wise mean using mean() function:
mean() function is applied to the required column through mapply() function, so that it calculates the mean of required column as shown below.
# mean() function in R : mean of multiple column in data frame mapply(mean,my_basket[,c(-1,-2)])
so the resultant mean of “Price” and “Tax” columns will be
Mean of the column by group using mean() function
aggregate() function along with the mean() function calculates the mean of a group. here mean of “Price” column, for “Item_Group” is calculated.
##### Mean of the column by group
aggregate(x= my_basket$Price,
by= list(my_basket$ITEM_GROUP),
FUN=mean)
Item_group has three groups “Dairy”,”Fruit” & “Vegetable”. mean of price for each group is calculated as shown below
Mean of the column by group and populate it by using mean() function:
group_by() function along with the mean() function calculates the mean of a group. here mean of “Price” column, for “Item_Group” is calculated and populated across as shown below
#### mean of the column by group and populate it using dplyr library(dplyr) my_basket %>% group_by(ITEM_GROUP) %>% mutate(mean_by_group = mean(Price))
Item_group has three groups “Dairy”,”Fruit” & “Vegetable”. mean of price for each group is calculated and populated as shown below
Row wise mean using mean() function along with dplyr
Row wise mean is calculated with the help rowwise() function of dplyr package and mean() function as shown below
## row wise mean using dplyr
library(dplyr)
my_basket %>%
rowwise() %>%
mutate(
Mean_price = mean(c(Price,Tax))
)
row wise mean of “Price” and “Tax” is calculated and populated for each row as shown below
For further understanding of mean() function in R using dplyr one can refer the dplyr documentation
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